Overview

Dataset statistics

Number of variables14
Number of observations10296
Missing cells372
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory172.8 B

Variable types

Numeric11
Categorical3

Alerts

BirthYear is highly correlated with MonthSalHigh correlation
MonthSal is highly correlated with BirthYearHigh correlation
CustMonVal is highly correlated with ClaimsRateHigh correlation
ClaimsRate is highly correlated with CustMonValHigh correlation
PremMotor is highly correlated with PremHousehold and 3 other fieldsHigh correlation
PremHousehold is highly correlated with PremMotorHigh correlation
PremHealth is highly correlated with PremMotorHigh correlation
PremLife is highly correlated with PremMotorHigh correlation
PremWork is highly correlated with PremMotorHigh correlation
BirthYear is highly correlated with MonthSalHigh correlation
MonthSal is highly correlated with BirthYearHigh correlation
CustMonVal is highly correlated with ClaimsRateHigh correlation
ClaimsRate is highly correlated with CustMonValHigh correlation
BirthYear is highly correlated with MonthSalHigh correlation
MonthSal is highly correlated with BirthYearHigh correlation
CustMonVal is highly correlated with ClaimsRateHigh correlation
ClaimsRate is highly correlated with CustMonValHigh correlation
PremMotor is highly correlated with PremHealth and 1 other fieldsHigh correlation
PremHealth is highly correlated with PremMotorHigh correlation
PremLife is highly correlated with PremMotorHigh correlation
CustMonVal is highly correlated with ClaimsRateHigh correlation
ClaimsRate is highly correlated with CustMonValHigh correlation
PremHealth is highly correlated with PremWorkHigh correlation
PremWork is highly correlated with PremHealthHigh correlation
PremLife has 104 (1.0%) missing values Missing
FirstPolYear is highly skewed (γ1 = 101.2958498) Skewed
CustMonVal is highly skewed (γ1 = -67.04273979) Skewed
ClaimsRate is highly skewed (γ1 = 71.20947447) Skewed
PremMotor is highly skewed (γ1 = 23.87096035) Skewed
PremHousehold is highly skewed (γ1 = 36.05402336) Skewed
PremHealth is highly skewed (γ1 = 84.51949178) Skewed
CustID is uniformly distributed Uniform
CustID has unique values Unique

Reproduction

Analysis started2021-11-07 15:31:41.909125
Analysis finished2021-11-07 15:32:06.241917
Duration24.33 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

CustID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct10296
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5148.5
Minimum1
Maximum10296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2021-11-07T15:32:06.384997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile515.75
Q12574.75
median5148.5
Q37722.25
95-th percentile9781.25
Maximum10296
Range10295
Interquartile range (IQR)5147.5

Descriptive statistics

Standard deviation2972.34352
Coefficient of variation (CV)0.5773222336
Kurtosis-1.2
Mean5148.5
Median Absolute Deviation (MAD)2574
Skewness0
Sum53008956
Variance8834826
MonotonicityStrictly increasing
2021-11-07T15:32:06.595996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
69181
 
< 0.1%
68601
 
< 0.1%
68611
 
< 0.1%
68621
 
< 0.1%
68631
 
< 0.1%
68641
 
< 0.1%
68651
 
< 0.1%
68661
 
< 0.1%
68671
 
< 0.1%
Other values (10286)10286
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
102961
< 0.1%
102951
< 0.1%
102941
< 0.1%
102931
< 0.1%
102921
< 0.1%
102911
< 0.1%
102901
< 0.1%
102891
< 0.1%
102881
< 0.1%
102871
< 0.1%

FirstPolYear
Real number (ℝ≥0)

SKEWED

Distinct26
Distinct (%)0.3%
Missing30
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1991.062634
Minimum1974
Maximum53784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2021-11-07T15:32:06.743279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1976
Q11980
median1986
Q31992
95-th percentile1996
Maximum53784
Range51810
Interquartile range (IQR)12

Descriptive statistics

Standard deviation511.2679127
Coefficient of variation (CV)0.2567814312
Kurtosis10262.56551
Mean1991.062634
Median Absolute Deviation (MAD)6
Skewness101.2958498
Sum20440249
Variance261394.8786
MonotonicityNot monotonic
2021-11-07T15:32:06.879590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1988512
 
5.0%
1994475
 
4.6%
1993473
 
4.6%
1989466
 
4.5%
1984464
 
4.5%
1986458
 
4.4%
1977453
 
4.4%
1978453
 
4.4%
1992451
 
4.4%
1990449
 
4.4%
Other values (16)5612
54.5%
ValueCountFrequency (%)
1974141
 
1.4%
1975285
2.8%
1976433
4.2%
1977453
4.4%
1978453
4.4%
1979443
4.3%
1980432
4.2%
1981445
4.3%
1982444
4.3%
1983423
4.1%
ValueCountFrequency (%)
537841
 
< 0.1%
1998112
 
1.1%
1997271
2.6%
1996440
4.3%
1995445
4.3%
1994475
4.6%
1993473
4.6%
1992451
4.4%
1991430
4.2%
1990449
4.4%

BirthYear
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)0.7%
Missing17
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1968.007783
Minimum1028
Maximum2001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2021-11-07T15:32:07.028251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1028
5-th percentile1941
Q11953
median1968
Q31983
95-th percentile1995
Maximum2001
Range973
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.70947624
Coefficient of variation (CV)0.01001493816
Kurtosis501.8127652
Mean1968.007783
Median Absolute Deviation (MAD)15
Skewness-10.53673466
Sum20229152
Variance388.4634537
MonotonicityNot monotonic
2021-11-07T15:32:07.302513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1962206
 
2.0%
1968200
 
1.9%
1964194
 
1.9%
1953193
 
1.9%
1981190
 
1.8%
1990189
 
1.8%
1974187
 
1.8%
1951186
 
1.8%
1952186
 
1.8%
1963186
 
1.8%
Other values (58)8362
81.2%
ValueCountFrequency (%)
10281
 
< 0.1%
193514
 
0.1%
193637
 
0.4%
193757
 
0.6%
193877
0.7%
1939101
1.0%
1940127
1.2%
1941145
1.4%
1942162
1.6%
1943163
1.6%
ValueCountFrequency (%)
200112
 
0.1%
200035
 
0.3%
199969
 
0.7%
199894
0.9%
1997133
1.3%
1996137
1.3%
1995155
1.5%
1994177
1.7%
1993155
1.5%
1992163
1.6%

EducDeg
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size692.2 KiB
3 - BSc/MSc
4799 
2 - High School
3510 
1 - Basic
1272 
4 - PhD
698 
 
17

Length

Max length15
Median length11
Mean length11.82721445
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 - High School
2nd row2 - High School
3rd row1 - Basic
4th row3 - BSc/MSc
5th row3 - BSc/MSc

Common Values

ValueCountFrequency (%)
3 - BSc/MSc4799
46.6%
2 - High School3510
34.1%
1 - Basic1272
 
12.4%
4 - PhD698
 
6.8%
17
 
0.2%

Length

2021-11-07T15:32:07.451986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-07T15:32:07.548874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
10279
29.9%
34799
14.0%
bsc/msc4799
14.0%
23510
 
10.2%
high3510
 
10.2%
school3510
 
10.2%
11272
 
3.7%
basic1272
 
3.7%
4698
 
2.0%
phd698
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthSal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3565
Distinct (%)34.7%
Missing36
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2506.667057
Minimum333
Maximum55215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2021-11-07T15:32:07.680610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile933.9
Q11706
median2501.5
Q33290.25
95-th percentile4046
Maximum55215
Range54882
Interquartile range (IQR)1584.25

Descriptive statistics

Standard deviation1157.449634
Coefficient of variation (CV)0.4617484524
Kurtosis474.3813076
Mean2506.667057
Median Absolute Deviation (MAD)791.5
Skewness11.25083378
Sum25718404
Variance1339689.656
MonotonicityNot monotonic
2021-11-07T15:32:07.829852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320010
 
0.1%
139810
 
0.1%
377610
 
0.1%
268710
 
0.1%
356010
 
0.1%
23089
 
0.1%
17669
 
0.1%
35689
 
0.1%
29599
 
0.1%
20739
 
0.1%
Other values (3555)10165
98.7%
(Missing)36
 
0.3%
ValueCountFrequency (%)
3333
< 0.1%
3341
 
< 0.1%
3353
< 0.1%
3361
 
< 0.1%
3401
 
< 0.1%
3412
< 0.1%
3421
 
< 0.1%
3441
 
< 0.1%
3461
 
< 0.1%
3481
 
< 0.1%
ValueCountFrequency (%)
552151
 
< 0.1%
344901
 
< 0.1%
50211
 
< 0.1%
49951
 
< 0.1%
49041
 
< 0.1%
48971
 
< 0.1%
48833
< 0.1%
48721
 
< 0.1%
48691
 
< 0.1%
48572
< 0.1%

GeoLivArea
Categorical

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size603.4 KiB
4.0
4145 
1.0
3048 
3.0
2066 
2.0
1036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.04145
40.3%
1.03048
29.6%
3.02066
20.1%
2.01036
 
10.1%
(Missing)1
 
< 0.1%

Length

2021-11-07T15:32:07.986989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-07T15:32:08.045873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4.04145
40.3%
1.03048
29.6%
3.02066
20.1%
2.01036
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Children
Categorical

Distinct2
Distinct (%)< 0.1%
Missing21
Missing (%)0.2%
Memory size603.0 KiB
1.0
7262 
0.0
3013 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07262
70.5%
0.03013
29.3%
(Missing)21
 
0.2%

Length

2021-11-07T15:32:08.157245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-07T15:32:08.236476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.07262
70.7%
0.03013
29.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CustMonVal
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct7012
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.8926049
Minimum-165680.42
Maximum11875.89
Zeros2
Zeros (%)< 0.1%
Negative2768
Negative (%)26.9%
Memory size80.6 KiB
2021-11-07T15:32:08.347475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-165680.42
5-th percentile-93.4875
Q1-9.44
median186.87
Q3399.7775
95-th percentile645.9325
Maximum11875.89
Range177556.31
Interquartile range (IQR)409.2175

Descriptive statistics

Standard deviation1945.811505
Coefficient of variation (CV)10.93812475
Kurtosis5323.18296
Mean177.8926049
Median Absolute Deviation (MAD)202.155
Skewness-67.04273979
Sum1831582.26
Variance3786182.414
MonotonicityNot monotonic
2021-11-07T15:32:08.513279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25272
 
2.6%
-3112
 
0.1%
-3711
 
0.1%
-3511
 
0.1%
-15.1110
 
0.1%
-12.3310
 
0.1%
-47.6710
 
0.1%
-33.899
 
0.1%
-10.339
 
0.1%
-21.119
 
0.1%
Other values (7002)9933
96.5%
ValueCountFrequency (%)
-165680.421
< 0.1%
-648911
< 0.1%
-52382.761
< 0.1%
-37327.081
< 0.1%
-28945.41
< 0.1%
-26130.451
< 0.1%
-14714.081
< 0.1%
-10198.911
< 0.1%
-10107.371
< 0.1%
-8719.041
< 0.1%
ValueCountFrequency (%)
11875.891
< 0.1%
5596.841
< 0.1%
4328.51
< 0.1%
2314.211
< 0.1%
2054.071
< 0.1%
1997.61
< 0.1%
1891.041
< 0.1%
1801.451
< 0.1%
17161
< 0.1%
1691.431
< 0.1%

ClaimsRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct165
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7427719503
Minimum0
Maximum256.2
Zeros58
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size80.6 KiB
2021-11-07T15:32:08.669295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.39
median0.72
Q30.98
95-th percentile1.1
Maximum256.2
Range256.2
Interquartile range (IQR)0.59

Descriptive statistics

Standard deviation2.916963637
Coefficient of variation (CV)3.927132192
Kurtosis5877.806759
Mean0.7427719503
Median Absolute Deviation (MAD)0.28
Skewness71.20947447
Sum7647.58
Variance8.508676861
MonotonicityNot monotonic
2021-11-07T15:32:08.831127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1457
 
4.4%
1.01212
 
2.1%
1.02203
 
2.0%
0.99198
 
1.9%
1.03195
 
1.9%
0.98173
 
1.7%
0.97148
 
1.4%
0.95143
 
1.4%
1.04140
 
1.4%
0.91139
 
1.4%
Other values (155)8288
80.5%
ValueCountFrequency (%)
058
0.6%
0.012
 
< 0.1%
0.034
 
< 0.1%
0.045
 
< 0.1%
0.058
 
0.1%
0.0620
 
0.2%
0.0713
 
0.1%
0.0837
0.4%
0.0939
0.4%
0.128
0.3%
ValueCountFrequency (%)
256.21
< 0.1%
961
< 0.1%
691
< 0.1%
631
< 0.1%
351
< 0.1%
32.31
< 0.1%
25.361
< 0.1%
15.651
< 0.1%
14.81
< 0.1%
13.91
< 0.1%

PremMotor
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1950
Distinct (%)19.0%
Missing34
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean300.4702524
Minimum-4.11
Maximum11604.42
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size80.6 KiB
2021-11-07T15:32:08.986815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4.11
5-th percentile63.68
Q1190.59
median298.61
Q3408.3
95-th percentile515.32
Maximum11604.42
Range11608.53
Interquartile range (IQR)217.71

Descriptive statistics

Standard deviation211.914997
Coefficient of variation (CV)0.7052777947
Kurtosis1096.286508
Mean300.4702524
Median Absolute Deviation (MAD)108.69
Skewness23.87096035
Sum3083425.73
Variance44907.96595
MonotonicityNot monotonic
2021-11-07T15:32:09.131616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398.7417
 
0.2%
361.2916
 
0.2%
246.4916
 
0.2%
206.1515
 
0.1%
279.6115
 
0.1%
409.5214
 
0.1%
269.9413
 
0.1%
346.5113
 
0.1%
312.0613
 
0.1%
210.2613
 
0.1%
Other values (1940)10117
98.3%
(Missing)34
 
0.3%
ValueCountFrequency (%)
-4.111
 
< 0.1%
1.781
 
< 0.1%
3.782
 
< 0.1%
4.781
 
< 0.1%
6.781
 
< 0.1%
6.891
 
< 0.1%
7.671
 
< 0.1%
8.675
< 0.1%
9.671
 
< 0.1%
9.782
 
< 0.1%
ValueCountFrequency (%)
11604.421
< 0.1%
8744.611
< 0.1%
5645.51
< 0.1%
4273.491
< 0.1%
4003.441
< 0.1%
3106.621
< 0.1%
585.221
< 0.1%
581.331
< 0.1%
580.111
< 0.1%
578.331
< 0.1%

PremHousehold
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct1061
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.4311917
Minimum-75
Maximum25048.8
Zeros60
Zeros (%)0.6%
Negative1097
Negative (%)10.7%
Memory size80.6 KiB
2021-11-07T15:32:09.300469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-75
5-th percentile-30
Q149.45
median132.8
Q3290.05
95-th percentile695.7
Maximum25048.8
Range25123.8
Interquartile range (IQR)240.6

Descriptive statistics

Standard deviation352.595984
Coefficient of variation (CV)1.675588021
Kurtosis2427.155944
Mean210.4311917
Median Absolute Deviation (MAD)103.35
Skewness36.05402336
Sum2166599.55
Variance124323.928
MonotonicityNot monotonic
2021-11-07T15:32:09.568889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.4562
 
0.6%
060
 
0.6%
-45.5560
 
0.6%
19.4560
 
0.6%
-30.5557
 
0.6%
-5.5556
 
0.5%
69.4555
 
0.5%
34.4554
 
0.5%
44.4554
 
0.5%
-40.5553
 
0.5%
Other values (1051)9725
94.5%
ValueCountFrequency (%)
-7518
 
0.2%
-7034
0.3%
-6535
0.3%
-6031
0.3%
-5527
0.3%
-5044
0.4%
-45.5560
0.6%
-4537
0.4%
-40.5553
0.5%
-4033
0.3%
ValueCountFrequency (%)
25048.81
< 0.1%
8762.81
< 0.1%
4130.71
< 0.1%
2223.751
< 0.1%
1957.61
< 0.1%
1924.251
< 0.1%
1918.151
< 0.1%
1826.451
< 0.1%
1777.552
< 0.1%
1748.11
< 0.1%

PremHealth
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1006
Distinct (%)9.8%
Missing43
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean171.5808329
Minimum-2.11
Maximum28272
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size80.6 KiB
2021-11-07T15:32:09.723107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2.11
5-th percentile54.12
Q1111.8
median162.81
Q3219.82
95-th percentile297.39
Maximum28272
Range28274.11
Interquartile range (IQR)108.02

Descriptive statistics

Standard deviation296.4059761
Coefficient of variation (CV)1.727500508
Kurtosis7914.203507
Mean171.5808329
Median Absolute Deviation (MAD)54.01
Skewness84.51949178
Sum1759218.28
Variance87856.50266
MonotonicityNot monotonic
2021-11-07T15:32:09.864292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.4730
 
0.3%
178.729
 
0.3%
159.1428
 
0.3%
158.1427
 
0.3%
147.3626
 
0.3%
112.9126
 
0.3%
169.726
 
0.3%
136.5825
 
0.2%
151.0325
 
0.2%
121.5825
 
0.2%
Other values (996)9986
97.0%
(Missing)43
 
0.4%
ValueCountFrequency (%)
-2.111
 
< 0.1%
5.781
 
< 0.1%
7.781
 
< 0.1%
11.671
 
< 0.1%
12.671
 
< 0.1%
14.672
< 0.1%
15.561
 
< 0.1%
15.672
< 0.1%
16.564
< 0.1%
16.672
< 0.1%
ValueCountFrequency (%)
282721
< 0.1%
7322.481
< 0.1%
17671
< 0.1%
1045.521
< 0.1%
442.861
< 0.1%
440.861
< 0.1%
432.971
< 0.1%
417.31
< 0.1%
417.081
< 0.1%
408.411
< 0.1%

PremLife
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct611
Distinct (%)6.0%
Missing104
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean41.85578199
Minimum-7
Maximum398.3
Zeros0
Zeros (%)0.0%
Negative668
Negative (%)6.5%
Memory size80.6 KiB
2021-11-07T15:32:10.033369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile-1.11
Q19.89
median25.56
Q357.79
95-th percentile140.36
Maximum398.3
Range405.3
Interquartile range (IQR)47.9

Descriptive statistics

Standard deviation47.480632
Coefficient of variation (CV)1.134386452
Kurtosis5.716367231
Mean41.85578199
Median Absolute Deviation (MAD)19.56
Skewness2.089846133
Sum426594.13
Variance2254.410416
MonotonicityNot monotonic
2021-11-07T15:32:10.208318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89130
 
1.3%
3.89121
 
1.2%
0.89119
 
1.2%
-1.11117
 
1.1%
12.89109
 
1.1%
6.89107
 
1.0%
5.89106
 
1.0%
4.89106
 
1.0%
7.89102
 
1.0%
1.89101
 
1.0%
Other values (601)9074
88.1%
(Missing)104
 
1.0%
ValueCountFrequency (%)
-765
0.6%
-664
0.6%
-574
0.7%
-456
0.5%
-372
0.7%
-273
0.7%
-1.11117
1.1%
-155
0.5%
-0.1192
0.9%
0.89119
1.2%
ValueCountFrequency (%)
398.31
< 0.1%
365.181
< 0.1%
363.291
< 0.1%
354.41
< 0.1%
346.41
< 0.1%
341.511
< 0.1%
330.841
< 0.1%
329.731
< 0.1%
324.841
< 0.1%
320.061
< 0.1%

PremWork
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct898
Distinct (%)8.8%
Missing86
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean41.2775142
Minimum-12
Maximum1988.7
Zeros0
Zeros (%)0.0%
Negative925
Negative (%)9.0%
Memory size80.6 KiB
2021-11-07T15:32:10.388860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-4
Q110.67
median25.67
Q356.79
95-th percentile137.3105
Maximum1988.7
Range2000.7
Interquartile range (IQR)46.12

Descriptive statistics

Standard deviation51.51357235
Coefficient of variation (CV)1.247981458
Kurtosis212.7789142
Mean41.2775142
Median Absolute Deviation (MAD)19.45
Skewness7.43811547
Sum421443.42
Variance2653.648136
MonotonicityNot monotonic
2021-11-07T15:32:10.603544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8976
 
0.7%
9.8972
 
0.7%
11.8970
 
0.7%
-5.1168
 
0.7%
-0.1166
 
0.6%
1.8965
 
0.6%
16.8965
 
0.6%
3.8964
 
0.6%
14.8964
 
0.6%
4.8963
 
0.6%
Other values (888)9537
92.6%
(Missing)86
 
0.8%
ValueCountFrequency (%)
-1234
0.3%
-1132
0.3%
-1032
0.3%
-929
0.3%
-851
0.5%
-739
0.4%
-6.1157
0.6%
-644
0.4%
-5.1168
0.7%
-548
0.5%
ValueCountFrequency (%)
1988.71
< 0.1%
930.441
< 0.1%
494.11
< 0.1%
451.531
< 0.1%
417.081
< 0.1%
353.181
< 0.1%
352.621
< 0.1%
352.511
< 0.1%
350.621
< 0.1%
339.841
< 0.1%

Interactions

2021-11-07T15:32:02.855353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:45.273185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.846298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.554239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.284880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.945705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.620521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.270718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.863390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.760064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.821380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:03.029588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:45.415513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.994301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.692398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.415848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.103389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.768258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.390394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.029712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.917065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.982650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:03.224263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:45.562831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.142300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.831105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.558351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.251190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.901801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.550422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.194966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:59.162221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.123905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:03.571836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:45.706834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.286808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.957160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.698321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.393117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.042835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.696490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.404035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:59.290900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.271904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:03.741016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:45.850638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.434108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.118290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.824596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.538261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.178752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.832648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.528472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:59.415897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.413437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:03.938291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.003635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.588204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.330472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.967267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.684912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.323507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.976845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.702217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:59.631016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.549894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:04.069821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.138237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.730360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.511473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.112184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.824759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.455687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.120804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:57.865216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:59.821014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.722013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:04.223582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.272866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:47.875719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.692808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.247982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:52.977854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.594009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.271761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.022257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.026493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:01.965012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:04.377654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.419886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.105750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.822898image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.402575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.134377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.732455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.414454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.144400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.206781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:02.198012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:04.535612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.554070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.247826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:49.971124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.542459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.298470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:54.875160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.554764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.364926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.348008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:02.417008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:04.785077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:46.700439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:48.404904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:50.135981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:51.796755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:53.460521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:55.018644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:56.705549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:31:58.529794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:00.571379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T15:32:02.656334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-11-07T15:32:10.878554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-07T15:32:11.156484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-07T15:32:11.416485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-07T15:32:11.636641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-07T15:32:11.789844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-07T15:32:05.085425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-07T15:32:05.555424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-07T15:32:05.841923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-07T15:32:06.122100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CustIDFirstPolYearBirthYearEducDegMonthSalGeoLivAreaChildrenCustMonValClaimsRatePremMotorPremHouseholdPremHealthPremLifePremWork
01.01985.01982.02 - High School2177.01.01.0380.970.39375.8579.45146.3647.0116.89
12.01981.01995.02 - High School677.04.01.0-131.131.1277.46416.20116.69194.48106.13
23.01991.01970.01 - Basic2277.03.00.0504.670.28206.15224.50124.5886.3599.02
34.01990.01981.03 - BSc/MSc1099.04.01.0-16.990.99182.4843.35311.1735.3428.34
45.01986.01973.03 - BSc/MSc1763.04.01.035.230.90338.6247.80182.5918.7841.45
56.01986.01956.02 - High School2566.04.01.0-24.331.00440.7518.90114.807.007.67
67.01979.01943.02 - High School4103.04.00.0-66.011.05156.92295.60317.9514.6726.34
78.01988.01974.02 - High School1743.04.01.0-144.911.13248.27397.30144.3666.6853.23
89.01981.01978.03 - BSc/MSc1862.01.01.0356.530.36344.5118.35210.048.789.89
910.01976.01948.03 - BSc/MSc3842.01.00.0-119.351.12209.26182.25271.9439.2355.12

Last rows

CustIDFirstPolYearBirthYearEducDegMonthSalGeoLivAreaChildrenCustMonValClaimsRatePremMotorPremHouseholdPremHealthPremLifePremWork
1028610287.01997.01943.03 - BSc/MSc3975.02.00.0220.270.62285.6177.25241.4931.458.89
1028710288.01996.01941.02 - High School3845.04.00.099.470.9087.35843.50121.58157.9233.45
1028810289.01982.01993.02 - High School1465.01.01.0795.150.3567.79820.15102.13182.4886.46
1028910290.01986.01943.02 - High School3498.04.00.0245.600.67227.82270.60160.92100.1369.90
1029010291.01994.01999.01 - Basic626.03.01.0176.260.856.89878.50103.13113.02201.26
1029110292.01984.01949.04 - PhD3188.02.00.0-0.110.96393.7449.45173.819.7814.78
1029210293.01977.01952.01 - Basic2431.03.00.01405.600.00133.581035.75143.2512.89105.13
1029310294.01994.01976.03 - BSc/MSc2918.01.01.0524.100.21403.63132.80142.2512.674.89
1029410295.01981.01977.01 - Basic1971.02.01.0250.050.65188.59211.15198.3763.90112.91
1029510296.01990.01981.04 - PhD2815.01.01.0463.750.27414.0894.45141.256.8912.89